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Anomaly Detection of Liquid Level in Mold during Continuous Casting by Using Forecasting and Error Generation

Authors :
Xiaojun Wu
Hongjia Kang
Sheng Yuan
Wenze Jiang
Qi Gao
Jinzhou Mi
Source :
Applied Sciences, Vol 13, Iss 13, p 7457 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Anomaly detection of liquid levels in molds is an important task in continuous casting. Data that consists of a series of liquid levels in mold during a continuous casting process can be viewed as a time series, on which Time Series Anomaly Detection (TSAD) methods can be applied. However, the abnormal and normal data in the liquid data in the mold sequence share similar features. And due to manual control limitations, the anomaly sequence in liquid level in mold data lasts longer. Therefore, using existing TSAD methods based on AutoEncoders (AEs) often results in high false positive rates. In this paper, a novel framework is proposed for anomaly detection of liquid level in mold by using unsupervised deep-learning-based TSAD. The framework decomposes a time series into normal and error sequences. A forecasting network reconstructs the normal sequence to solve the first issue, which allows the proposed method to consider the context. An error extraction network generates errors from the original sequence to solve the second issue. It removes anomalies from the original sequence during training to prevent anomaly pollution and allows the forecasting network’s training to be free from anomaly pollution. A new dynamic threshold method is proposed to identify anomalies. The proposed method is evaluated on the actual casting dataset by comparing it with baseline methods. The experiment results indicate that the proposed framework outperforms some of the best anomaly detection methods in terms of accuracy, precision, and F1 score.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
13
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.210b20d981554971bce9f6cb52b5d7de
Document Type :
article
Full Text :
https://doi.org/10.3390/app13137457